Optimization Based Self-localization for IoT Wireless Sensor Networks

In this paper we propose an embedded optimization framework for the simultaneous self-localization of all sensors in wireless sensor networks making use of range measurements from ultra-wideband (UWB) signals. Low-power UWB radios, which provide time-of-arrival measurements with decimeter accuracy over large distances, have been increasingly envisioned for realtime localization of IoT devices in GPS-denied environments and large sensor networks. In this work, we therefore explore different non-linear least-squares optimization problems to formulate the localization task based on UWB range measurements. We solve the resulting optimization problems directly using non-linear-programming algorithms that guarantee convergence to locally optimal solutions. This optimization framework allows the consistent comparison of different optimization methods for sensor localization. We propose and demonstrate the best optimization approach for the self-localization of sensors equipped with off-the-shelf microcontrollers using state-of-the-art code generation techniques for the plug-and-play deployment of the optimal localization algorithm. Numerical results indicate that the proposed approach improves localization accuracy and decreases computation times relative to existing iterative methods

Parameter selection and performance comparison of particle swarm optimization in sensor networks localization

Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors\u27 memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm

Cyber-Physical Codesign of Distributed Structural Health Monitoring with Wireless Sensor Networks

Our Deteriorating Civil Infrastructure Faces the Critical Challenge of Long-Term Structural Health Monitoring for Damage Detection and Localization. in Contrast to Existing Research that Often Separates the Designs of Wireless Sensor Networks and Structural Engineering Algorithms, This Paper Proposes a Cyber-Physical Codesign Approach to Structural Health Monitoring based on Wireless Sensor Networks. Our Approach Closely Integrates 1) Flexibility-Based Damage Localization Methods that Allow a Tradeoff between the Number of Sensors and the Resolution of Damage Localization, and 2) an Energy-Efficient, Multilevel Computing Architecture Specifically Designed to Leverage the Multiresolution Feature of the Flexibility-Based Approach. the Proposed Approach Has Been Implemented on the Intel Imote2 Platform. Experiments on a Simulated Truss Structure and a Real Full-Scale Truss Structure Demonstrate the System\u27s Efficacy in Damage Localization and Energy Efficiency

Cyber-Physical Codesign of Distributed Structural Health Monitoring with Wireless Sensor Networks

Our Deteriorating Civil Infrastructure Faces the Critical Challenge of Long-Term Structural Health Monitoring for Damage Detection and Localization. in Contrast to Existing Research that Often Separates the Designs of Wireless Sensor Networks and Structural Engineering Algorithms, This Paper Proposes a Cyber-Physical Co-Design Approach to Structural Health Monitoring based on Wireless Sensor Networks. Our Approach Closely Integrates (1) Flexibility-Based Damage Localization Methods that Allow a Tradeoff between the Number of Sensors and the Resolution of Damage Localization, and (2) an Energy-Efficient, Multi-Level Computing Architecture Specifically Designed to Leverage the Multi-Resolution Feature of the Flexibility-Based Approach. the Proposed Approach Has Been Implemented on the Intel Imote2 Platform. Experiments on a Physical Beam and Simulations of a Truss Structure Demonstrate the System\u27s Efficacy in Damage Localization and Energy Efficiency. © 2010 ACM

Malicious and Malfunctioning Node Detection via Observed Physical Layer Data

There are many mechanisms that can cause inadequate or unreliable information in sensor networks. A user of the network might be interested in detecting and classifying specific sensors nodes causing these problems. Several network layer based trust methods have been developed in previous research to assess these issues; in contrast this work develops a trust protocol based on observations of physical layer data collected by the sensors. Observations of physical layer data are used for decisions and calculations, and are based on just the measurements collected by the sensors. Although this information is packaged and distributed on the network layer, only the physical measurement is considered. This protocol is used to detect faulty nodes operating in the sensor network. The context of this research is Wireless Network Discovery (WND), which refers to modeling all layers of a non-cooperative wireless network. The focus in particular is the localization of transmitters, and detection of sensors affecting the localization. To accomplish this, a model for faulty sensors and two methods of detection are developed. Detection rates are analyzed with Receiver Operating Characteristic (ROC) curves, and the trade-off of detection versus localization error is discussed. Classification between faulty sensors is also considered to determine appropriate response to potential network attacks

Experimental evaluation in wireless communications

This editorial sums up relevant topics on the assessment of wireless communication systems covered by the especial issue entitled "Experimental Evaluation in Wireless Communications". The topics include practical aspects on the implementation of distributed asynchronous non-linear kernel methods over wireless sensor networks; localization methods based on the exploitation of radio-frequency identification (RFID) wireless sensors and cellular networks or on sparsity approximations; channel sounding and assessment of broadband orthogonal frequency-division multiplexing (OFDM)-based wireless systems in high-speed vehicular communications; coexistence analysis of femtocell-based and outdoor-to-indoor systems; techniques for peak-to-average power ratio (PAPR) reduction; new solutions for baseband and radio frequency (RF) hardware impairments in full-duplex wireless systems; and, finally, suitability of interference alignment for broadband indoor wireless communications

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial